Subsurface imaging of rigid particles buried in a polymer matrix based on atomic force microscopy mechanical sensing

Abstract

Several subsurface imaging methods based on atomic force microscopy (AFM) linear nanomechanical mapping, namely contact resonance (CR), bimodal and harmonic AFMs, are investigated and compared. Their respective subsurface detection capability is estimated and evaluated on a model specimen, which is prepared by embedding SiO2 microparticles in a PDMS elastomer. The measured CR frequency, bimodal and harmonic amplitudes are related to local mechanical properties by analyzing cantilever dynamics and further linked to subsurface depths of the particles by finite element analysis. The maximum detectable depths are obtained from the apparent particle diameters in subsurface image channels via employing a simple geometrical model. Under common experimental settings, results demonstrate that the depth limits reach up to about 812 nm, 212 nm and 127 nm for CR, bimodal and harmonic AFM modes, respectively. The depth sensitivity can be tuned and optimized by using either different cantilever eigenmodes in CR-AFM or spectroscopy analysis in bimodal and harmonic AFMs. The three imaging methods have their own suitable application situations. The comparisons can advance a further step into understanding the subsurface image contrast via AFM mechanical sensing.